Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/320304 
Year of Publication: 
2024
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 15 [Issue:] 2 [Year:] 2024 [Pages:] 523-570
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
All parameters in structural vector autoregressive (SVAR) models are locally identified when the structural shocks are independent and follow non-Gaussian distributions. Unfortunately, standard inference methods that exploit such features of the data for identification fail to yield correct coverage for structural functions of the model parameters when deviations from Gaussianity are small. To this extent, we propose a locally robust semiparametric approach to conduct hypothesis tests and construct confidence sets for structural functions in SVAR models. The methodology fully exploits non-Gaussianity when it is present, but yields correct size/coverage for local-to-Gaussian densities. Empirically, we revisit two macroeconomic SVAR studies where we document mixed results. For the oil price model of Kilian and Murphy (2012), we find that non-Gaussianity can robustly identify reasonable confidence sets, whereas for the labor supply-demand model of Baumeister and Hamilton (2015) this is not the case. Moreover, these exercises highlight the importance of using weak identification robust methods to assess estimation uncertainty when using non-Gaussianity for identification.
Subjects: 
Weak identification
semiparametric inference
hypothesis testing,impulse responses
independent component analysis
JEL: 
C32
C39
C51
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.